Cited 0 time in
Cited 0 time in
Intelligent sensor attack detection and identification for automotive cyber-physical systems
- Intelligent sensor attack detection and identification for automotive cyber-physical systems
- Shin, Jong Ho; Baek, Young Mi; Eun, Yong Soon; Son, Sang Hyuk
- DGIST Authors
- Eun, Yong Soon; Son, Sang Hyuk
- Issue Date
- 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017, 1-8
- This paper addresses the problem of detection and identification of the sensor attacks when most sensors are attacked. Sensors can play a key role to improve safety and convenience in automotive Cyber-Physical Systems (CPS). A dramatic increase in connectivity and openness of the automotive CPS brings high security risks. If multiple and heterogeneous sensors equipped for braking and steering provides false sensing information for their controllers under deception attacks, it might cause catastrophic situations during driving. If the existing machine learning approaches are applied for sensor attacks while the majority of sensors is attacked, it cannot guarantee to identify deceptions as cyber-physical attacks. To address this problem, we propose an intelligent sensor attack detection and identification method based on Deep Neural Network (DNN) techniques, called deep learning, without a prior knowledge about the deception attacks modifying sensing data in time. We investigate an autonomous vehicle with Inertial Measurement Unit (IMU) and wheel encoder sensors under conditions of uncertainty and nonlinearity during driving. We firstly identify all possible attacks category on the sensors of it, choose what model to use and then systematically design its architecture on which the performance of deep learning highly depends. We train and then validate the proposed method's performance on real measurement data obtained from an unmanned ground vehicle. Finally, we show analytically the superiority of our method in terms of accuracy, precision, and computation time, including the worst situation where two among three sensors are simultaneously attacked. © 2017 IEEE.
- Institute of Electrical and Electronics Engineers Inc.
- Related Researcher
RTCPS(Real-Time Cyber-Physical Systems) Lab
There are no files associated with this item.
- Department of Information and Communication EngineeringDSC Lab(Dynamic Systems and Control Laboratory)2. Conference Papers
Department of Information and Communication EngineeringRTCPS(Real-Time Cyber-Physical Systems) Lab2. Conference Papers
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.